R Notebook
John Little
2018-08-29
library(sf)Linking to GEOS 3.6.1, GDAL 2.2.3, proj.4 4.9.3library(tidyverse)[37m-- [1mAttaching packages[22m --------------------------------------- tidyverse 1.2.1 --[39m
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[31mx[37m [34mdplyr[37m::[32mlag()[37m masks [34mstats[37m::lag()[39mlibrary(tigris)To enable
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
Attaching package: 㤼㸱tigris㤼㸲
The following object is masked from 㤼㸱package:graphics㤼㸲:
plotTEST Approach
Get Shapes
Get shapefile for US
ny_area <- states(class = "sf", year = 2000)
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|=================================================================================================| 100%filter to NY and NJ
ny_area <- ny_area %>%
filter(NAME00 == "New York" |
NAME00 == "New Jersey" |
NAME00 == "Pennsylvania") View sf.dataframe
as_tibble(ny_area)Zoom in: ny_area
tri_state <- ggplot() +
geom_sf(data = ny_area, aes(fill = NAME00)) +
coord_sf(xlim = c(-73, -75), ylim = c(40, 41.5))
tri_state
Assign colors to states
ggplot() +
geom_sf(data = ny_area, aes(fill = NAME00)) +
coord_sf(xlim = c(-73, -75), ylim = c(40, 41.5)) +
scale_fill_grey(start=1, end=0, name="% HCA")+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="lightskyblue1"),
plot.background = element_rect(fill="lightskyblue1", color="black", size=1),
axis.line = element_blank(),
legend.key=element_rect(colour="black"),
legend.background=element_rect(colour="black"))
Apply with Test Data
Thsi data approximates Ben’s examples
Get NYC Area Tracts Polygons by County.
The counties New York City are:
- 005 - Bronx
- 047 - Kings (Brooklyn)
- 061 - New York (Manhattan)
- 081 - Queens
- 085 - Richmond (Staten Island)
manhattan <- tigris::tracts("NY", county = 061, year = 2000, class = "sf")Using FIPS code '36' for state 'NY'
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|=================================================================================================| 100%bronx <- tigris::tracts("NY", county = 005, year = 2000, class = "sf")Using FIPS code '36' for state 'NY'brooklyn <- tigris::tracts("NY", county = 047, year = 2000, class = "sf")Using FIPS code '36' for state 'NY'queens <- tigris::tracts("NY", county = 081, year = 2000, class = "sf")Using FIPS code '36' for state 'NY'staten <- tigris::tracts("NY", county = 085, year = 2000, class = "sf")Using FIPS code '36' for state 'NY' boroughs <- rbind(manhattan, bronx, brooklyn, queens, staten)
Plan to left_join on borroughts$CTIDFP00
as_tibble(boroughs)Mock A Data Variable
Get Census Pop for Bororoughs, 2000
plan to lef_join on boro_pop$Geo_FIPS
boro_pop <- read_csv("data/boro_pop_2000.csv") Parsed with column specification:
cols(
Geo_NAME = col_character(),
Geo_QName = col_character(),
Geo_FIPS = col_double(),
Geo_SUMLEV = col_integer(),
Geo_GEOCOMP = col_character(),
Geo_STATECE = col_integer(),
Geo_STATE = col_integer(),
Geo_COUNTY = col_character(),
Geo_COUNTYSC = col_integer(),
Geo_COUSUB = col_character(),
Geo_TRACT = col_character(),
SE_T001_001 = col_integer()
)boro_pop <- boro_pop %>%
mutate(CTIDFP00 = as.character(Geo_FIPS)) %>%
rename(pop = SE_T001_001) %>%
select(CTIDFP00, pop)
boro_popleft_join
left_join(boroughs, boro_pop, by = "CTIDFP00") -> boroughs
as_tibble(boroughs)Map it
ggplot() +
geom_sf(data = boroughs, aes(fill = pop)) +
viridis::scale_fill_viridis() +
coord_sf(xlim = c(-73.6, -74.3), ylim = c(40.4, 41)) +
theme_bw()
Layering
first_pass <- ggplot() +
geom_sf(data = ny_area, aes(), color = "black", fill = "burlywood4") +
geom_sf(data = boroughs, aes(fill = pop)) +
viridis::scale_fill_viridis() +
coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95))
first_pass
Add Water Shading
first_pass +
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_line(color = "transparent"),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="lightskyblue1"),
plot.background = element_rect(fill="lightskyblue1", color="black", size=1),
axis.line = element_blank(),
legend.key=element_rect(colour="black"),
legend.background=element_rect(colour="black"))
NABlack & White
Reverse viridis direction for greyscale.
ggplot() +
geom_sf(data = ny_area, aes(), color = "black", fill = "white") +
geom_sf(data = boroughs, aes(fill = pop)) +
viridis::scale_fill_viridis(direction = -1) +
coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) +
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_line(color = "transparent"),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="lightskyblue1"),
plot.background = element_rect(fill="lightskyblue1", color="black", size=1),
axis.line = element_blank())
Coastline
shapefile from: http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=928
coast <- read_sf("data/hydrography_shp/AreaHydrography.shp")as_tibble(coast)
Simple feature collection with 19974 features and 5 fields
geometry type: POLYGON
dimension: XY
bbox: xmin: 107250.5 ymin: 4481030 xmax: 770761.8 ymax: 4985490
epsg (SRID): 26918
proj4string: +proj=utm +zone=18 +datum=NAD83 +units=m +no_defsProblem trying to crop. Need to consult with Mark due to the following errors:
- although coordinates are longitude/latitude, st_intersection assumes that they are planar
- attribute variables are assumed to be spatially constant throughout all geometries
resources may include:
- https://r-spatial.github.io/sf/articles/sf6.html#although-coordinates-are-longitudelatitude-xxx-assumes-that-they-are-planar
- https://r-spatial.github.io/sf/articles/sf1.html#geometrycollection
- https://r-spatial.github.io/sf/reference/st_crs.html
- https://r-spatial.github.io/sf/reference/st_bbox.html
- https://r-spatial.github.io/sf/reference/st_crop.html
- https://gis.stackexchange.com/questions/282524/cropping-sf-object-in-r
- https://stackoverflow.com/questions/50140707/how-to-merge-a-shapefile-with-a-dataframe-with-latitude-longitude-data
coast1 <- st_set_crs(coast, st_crs(boroughs))
st_crs(coast)
st_crs(coast1)
st_crs(boroughs)
coast1 <- st_crop(coast1, st_bbox(boroughs))
foo <- st_intersection(boroughs, coast1)
ggplot() +
geom_sf(data = coast1)
st_bbox(boroughs)
st_crs(st_set_crs(coast, st_crs(boroughs)))No Cropping
nycoast <- ggplot() +
geom_sf(data = coast)
nycoast
3 Layers
Borough choropleth + Sate Area boundaries + Hydrography
ggplot() +
geom_sf(data = ny_area, aes(), color = "black", fill = "white") +
geom_sf(data = borroughs, aes(fill = pop)) +
viridis::scale_fill_viridis(direction = -1) +
geom_sf(data = coast, color = "black", fill = "black") +
coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) +
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_line(color = "transparent"),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="lightskyblue1"),
plot.background = element_rect(fill="lightskyblue1", color="black", size=1),
axis.line = element_blank())
Final
removed state boundaries. population choropleth and hydrography remain.
ggplot() +
geom_sf(data = borroughs, aes(fill = pop)) +
viridis::scale_fill_viridis(direction = -1) +
geom_sf(data = coast, color = "black", fill = "black") +
coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) +
theme(panel.background = element_rect(fill = "transparent"),
panel.grid.major = element_line(color = "transparent"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
---
title: "R Notebook"
author: "John Little"
date: "`r Sys.Date()`"
output: html_notebook
---

```{r}
library(sf)
library(tidyverse)
library(tigris)
```


## TEST Approach

### Get Shapes

Get shapefile for US

```{r}
ny_area <- states(class = "sf", year = 2000)
```

filter to NY and NJ

```{r}
ny_area <- ny_area %>% 
  filter(NAME00 == "New York" |
           NAME00 == "New Jersey" |
           NAME00 == "Pennsylvania") 
```

View sf.dataframe

```{r}
as_tibble(ny_area)


```

### Zoom in: ny_area

```{r}
tri_state <- ggplot() +
  geom_sf(data = ny_area, aes(fill = NAME00)) +
  coord_sf(xlim = c(-73, -75), ylim = c(40, 41.5))

tri_state
```

### Assign colors to states

```{r}

ggplot() +
  geom_sf(data = ny_area, aes(fill = NAME00)) +
  coord_sf(xlim = c(-73, -75), ylim = c(40, 41.5)) +
  scale_fill_grey(start=1, end=0, name="% HCA")+
   theme(axis.text.x = element_blank(), 
  	     axis.text.y = element_blank(),
  	     axis.ticks = element_blank(), 
         axis.title.x = element_blank(), 
         axis.title.y = element_blank(),
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill="lightskyblue1"), 
         plot.background = element_rect(fill="lightskyblue1", color="black", size=1), 
         axis.line = element_blank(),
         legend.key=element_rect(colour="black"),
         legend.background=element_rect(colour="black"))
```

## Apply with Test Data

Thsi data approximates Ben's examples

### Get NYC Area Tracts Polygons by County.  

The counties New York City are:

- 005 - Bronx
- 047 - Kings (Brooklyn)
- 061 - New York (Manhattan)
- 081 - Queens
- 085 - Richmond (Staten Island)

```{r}
manhattan <- tigris::tracts("NY", county = 061, year = 2000, class = "sf")
bronx <- tigris::tracts("NY", county = 005, year = 2000, class = "sf")
brooklyn <- tigris::tracts("NY", county = 047, year = 2000, class = "sf")
queens <- tigris::tracts("NY", county = 081, year = 2000, class = "sf")
staten <- tigris::tracts("NY", county = 085, year = 2000, class = "sf")
  
```


```{r}
boroughs <- rbind(manhattan, bronx, brooklyn, queens, staten)
  
```

Plan to left_join on `borroughts$CTIDFP00`

```{r}
as_tibble(boroughs)
```

### Mock A Data Variable

Get Census Pop for Bororoughs, 2000 

plan to lef_join on `boro_pop$Geo_FIPS`

```{r}
boro_pop <- read_csv("data/boro_pop_2000.csv") 
boro_pop <- boro_pop %>% 
  mutate(CTIDFP00 = as.character(Geo_FIPS)) %>% 
  rename(pop = SE_T001_001) %>% 
  select(CTIDFP00, pop) 


boro_pop
```

### left_join
```{r}
left_join(boroughs, boro_pop, by = "CTIDFP00") -> boroughs

as_tibble(boroughs)

```


### Map it


```{r message=FALSE, warning=FALSE}
ggplot() +
  geom_sf(data = boroughs, aes(fill = pop)) +
  viridis::scale_fill_viridis() +
  coord_sf(xlim = c(-73.6, -74.3), ylim = c(40.4, 41)) +
  theme_bw()
```

## Layering

```{r}
first_pass <- ggplot() +
  geom_sf(data = ny_area, aes(), color = "black", fill = "burlywood4") +
  geom_sf(data = boroughs, aes(fill = pop)) +
  viridis::scale_fill_viridis() +
    coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95))

first_pass
```

### Add Water Shading

```{r}
first_pass +
   theme(axis.text.x = element_blank(), 
  	     axis.text.y = element_blank(),
  	     axis.ticks = element_blank(), 
         axis.title.x = element_blank(), 
         axis.title.y = element_blank(),
         panel.grid.major = element_line(color = "transparent"),  
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill="lightskyblue1"),
         plot.background = element_rect(fill="lightskyblue1", color="black", size=1), 
         axis.line = element_blank(),
         legend.key=element_rect(colour="black"),
         legend.background=element_rect(colour="black"))
  
```

## Black & White

Reverse viridis direction for greyscale.

```{r}
ggplot() +
  geom_sf(data = ny_area, aes(), color = "black", fill = "white") +
  geom_sf(data = boroughs, aes(fill = pop)) +
  viridis::scale_fill_viridis(direction = -1) +
  coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) +
  theme(axis.text.x = element_blank(), 
  	    axis.text.y = element_blank(),
  	    axis.ticks = element_blank(), 
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        panel.grid.major = element_line(color = "transparent"),  
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill="lightskyblue1"),
        plot.background = element_rect(fill="lightskyblue1", color="black", size=1), 
        axis.line = element_blank())
```

## Coastline

shapefile from:  http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=928 

```{r}
coast <- read_sf("data/hydrography_shp/AreaHydrography.shp")
```

```{r}
as_tibble(coast)
```

### Problem trying to crop.  Need to consult with Mark due to the following errors:

- although coordinates are **longitude/latitude**, st_intersection assumes that they are **planar**
- attribute variables are assumed to be spatially constant throughout all geometries

resources may include:

- https://r-spatial.github.io/sf/articles/sf6.html#although-coordinates-are-longitudelatitude-xxx-assumes-that-they-are-planar
- https://r-spatial.github.io/sf/articles/sf1.html#geometrycollection 
- https://r-spatial.github.io/sf/reference/st_crs.html 
- https://r-spatial.github.io/sf/reference/st_bbox.html 
- https://r-spatial.github.io/sf/reference/st_crop.html 
- https://gis.stackexchange.com/questions/282524/cropping-sf-object-in-r
- https://stackoverflow.com/questions/50140707/how-to-merge-a-shapefile-with-a-dataframe-with-latitude-longitude-data
- 

``` r
coast1 <- st_set_crs(coast, st_crs(boroughs))

st_crs(coast)
st_crs(coast1)
st_crs(boroughs)

coast1 <- st_crop(coast1, st_bbox(boroughs))

foo <- st_intersection(boroughs, coast1)

ggplot() +
  geom_sf(data = coast1)

st_bbox(boroughs)


st_crs(st_set_crs(coast, st_crs(boroughs)))
```
### No Cropping

```{r}
nycoast <- ggplot() +
  geom_sf(data = coast)

nycoast
```

## 3 Layers

Borough choropleth + Sate Area boundaries + Hydrography

```{r}
ggplot() +
  geom_sf(data = ny_area, aes(), color = "black", fill = "white") +
  geom_sf(data = boroughs, aes(fill = pop)) +
  viridis::scale_fill_viridis(direction = -1) +
  geom_sf(data = coast, color = "black", fill = "black") +
  coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) +
  theme(axis.text.x = element_blank(), 
  	    axis.text.y = element_blank(),
  	    axis.ticks = element_blank(), 
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        panel.grid.major = element_line(color = "transparent"),  
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill="lightskyblue1"),
        plot.background = element_rect(fill="lightskyblue1", color="black", size=1), 
        axis.line = element_blank())
```

## Final

removed state boundaries.  population choropleth and hydrography remain.

```{r}
ggplot() +
  geom_sf(data = boroughs, aes(fill = pop)) +
  viridis::scale_fill_viridis(direction = -1) +
  geom_sf(data = coast, color = "black", fill = "black") +
  coord_sf(xlim = c(-73.7, -74.3), ylim = c(40.45, 40.95)) + 
  theme(panel.background = element_rect(fill = "transparent"), 
        panel.grid.major = element_line(color = "transparent"), 
        axis.text.x = element_blank(), 
        axis.text.y = element_blank(), 
        axis.ticks = element_blank())
```
